Machine learning (ML) is a subarea of artificial intelligence (AI) that is concerned with the
question of how to construct computer programs that automatically improve with experience. In recent years, many successful machine learning applications have been developed, including data mining programs that learn to detect fraudulent credit card transactions, information-filtering systems that learn users' reading preferences, face-recognition systems that learn
to automatically identify people, biological sequence analysis programs that learn how to search massive databases for proteins belonging to certain families, and autonomous vehicles that learn to drive on public highways. At the same time, there have been important advances in the theory and algorithms that form the foundations of this field.
The goal of this course is to present the fundamentals that form the core of machine learning. The topics the course will cover include decision trees, neural networks, support vector machines, hypothesis evaluation and ROC analysis, Bayesian learning, instance-based learning, and (time permitting) genetic algorithms and reinforcement learning. At the end of the course, students will sufficiently understand the fundamentals of these areas of machine learning to begin basic research in the area and to understand the ideas of technical papers in machine learning.
This course will be delivered on-line. Course grades will be based on completion of lab assignments using existing ML libraries such as Python’s scikit-learn. The only prerequisite is programming competence.
Catalog Description:
Introduction to the fundamentals and current trends in machine learning. Possible applications for game playing, text categorization, speech recognition, automatic system control, date mining, computational biology, and robotics. Theoretical and empirical analyses of decision trees, artificial neural networks, Bayesian classifiers, genetic algorithms, instance-based classifiers and reinforcement learning.